Impact of response variability on Pareto front optimization
Jessica L. Chapman, Lu Lu, Christine M. Anderson‐Cook · Statistical Analysis and Data Mining The ASA Data Science Journal · 2015
Abstract A two‐stage Pareto front approach can improve the process of making a decision about which input values simultaneously optimize multiple responses. However, ignoring estimation uncertainty and natural variability in the responses can potentially lead to suboptimal choices about those input values. A simulation‐based approach is used to quantify and examine the impact that variability has on the superior solutions identified on the Pareto front and their performance. Because each optimization scenario has its own unique characteristics, including responses with different amounts of natural variability, the impact of variability on the solutions varies from situation to situation. We study how varying the amount of response variability affects the locations identified for the front and the characteristics of the most promising solutions on the front. We illustrate the method with an application involving process improvement through variance reduction.